(kuberay-rayjob-quickstart)=

RayJob Quickstart¶

Prerequisites¶

• KubeRay v0.6.0 or higher
  • KubeRay v0.6.0 or v1.0.0: Ray 1.10 or higher.
  • KubeRay v1.1.1 or newer is highly recommended: Ray 2.8.0 or higher.

What's a RayJob?¶

A RayJob manages two aspects:

• RayCluster: A RayCluster custom resource manages all Pods in a Ray cluster, including a head Pod and multiple worker Pods.
• Job: A Kubernetes Job runs ray job submit to submit a Ray job to the RayCluster.

What does the RayJob provide?¶

With RayJob, KubeRay automatically creates a RayCluster and submits a job when the cluster is ready. You can also configure RayJob to automatically delete the RayCluster once the Ray job finishes.

To understand the following content better, you should understand the difference between:

• RayJob: A Kubernetes custom resource definition provided by KubeRay.
• Ray job: A Ray job is a packaged Ray application that can run on a remote Ray cluster. See this document for more details.
• Submitter: The submitter is a Kubernetes Job that runs ray job submit to submit a Ray job to the RayCluster.

RayJob Configuration¶

• RayCluster configuration

  • rayClusterSpec - Defines the RayCluster custom resource to run the Ray job on.
  • clusterSelector - Use existing RayCluster custom resources to run the Ray job instead of creating a new one. See ray-job.use-existing-raycluster.yaml for example configurations.

• Ray job configuration

  • entrypoint - The submitter runs ray job submit --address ... --submission-id ... -- $entrypoint to submit a Ray job to the RayCluster.
  • runtimeEnvYAML (Optional): A runtime environment that describes the dependencies the Ray job needs to run, including files, packages, environment variables, and more. Provide the configuration as a multi-line YAML string.

  Example:

  spec:
    runtimeEnvYAML: |
      pip:
        - requests==2.26.0
        - pendulum==2.1.2
      env_vars:
        KEY: "VALUE"
  

  See {ref}Runtime Environments <runtime-environments> for more details. (New in KubeRay version 1.0.0)

  • jobId (Optional): Defines the submission ID for the Ray job. If not provided, KubeRay generates one automatically. See {ref}Ray Jobs CLI API Reference <ray-job-submission-cli-ref> for more details about the submission ID.
  • metadata (Optional): See {ref}Ray Jobs CLI API Reference <ray-job-submission-cli-ref> for more details about the --metadata-json option.
  • entrypointNumCpus / entrypointNumGpus / entrypointResources (Optional): See {ref}Ray Jobs CLI API Reference <ray-job-submission-cli-ref> for more details.
  • backoffLimit (Optional, added in version 1.2.0): Specifies the number of retries before marking this RayJob failed. Each retry creates a new RayCluster. The default value is 0.

• Submission configuration

  • submissionMode (Optional): submissionMode specifies how RayJob submits the Ray job to the RayCluster. In "K8sJobMode", the KubeRay operator creates a submitter Kubernetes Job to submit the Ray job. In "HTTPMode", the KubeRay operator sends a request to the RayCluster to create a Ray job. The default value is "K8sJobMode".
  • submitterPodTemplate (Optional): Defines the Pod template for the submitter Kubernetes Job. This field is only effective when submissionMode is "K8sJobMode".
    • RAY_DASHBOARD_ADDRESS - The KubeRay operator injects this environment variable to the submitter Pod. The value is $HEAD_SERVICE:$DASHBOARD_PORT.
    • RAY_JOB_SUBMISSION_ID - The KubeRay operator injects this environment variable to the submitter Pod. The value is the RayJob.Status.JobId of the RayJob.
    • Example: ray job submit --address=http://$RAY_DASHBOARD_ADDRESS --submission-id=$RAY_JOB_SUBMISSION_ID ...
    • See ray-job.sample.yaml for more details.
  • submitterConfig (Optional): Additional configurations for the submitter Kubernetes Job.
    • backoffLimit (Optional, added in version 1.2.0): The number of retries before marking the submitter Job as failed. The default value is 2.

• Automatic resource cleanup

  • shutdownAfterJobFinishes (Optional): Determines whether to recycle the RayCluster after the Ray job finishes. The default value is false.
  • ttlSecondsAfterFinished (Optional): Only works if shutdownAfterJobFinishes is true. The KubeRay operator deletes the RayCluster and the submitter ttlSecondsAfterFinished seconds after the Ray job finishes. The default value is 0.
  • activeDeadlineSeconds (Optional): If the RayJob doesn't transition the JobDeploymentStatus to Complete or Failed within activeDeadlineSeconds, the KubeRay operator transitions the JobDeploymentStatus to Failed, citing DeadlineExceeded as the reason.
  • DELETE_RAYJOB_CR_AFTER_JOB_FINISHES (Optional, added in version 1.2.0): Set this environment variable for the KubeRay operator, not the RayJob resource. If you set this environment variable to true, the RayJob custom resource itself is deleted if you also set shutdownAfterJobFinishes to true. Note that KubeRay deletes all resources created by the RayJob, including the Kubernetes Job.

• Others

  • suspend (Optional): If suspend is true, KubeRay deletes both the RayCluster and the submitter. Note that Kueue also implements scheduling strategies by mutating this field. Avoid manually updating this field if you use Kueue to schedule RayJob.
  • deletionPolicy (Optional, alpha in v1.3.0): Indicates what resources of the RayJob are deleted upon job completion. Valid values are DeleteCluster, DeleteWorkers, DeleteSelf or DeleteNone. If unset, deletion policy is based on spec.shutdownAfterJobFinishes. This field requires the RayJobDeletionPolicy feature gate to be enabled.
    • DeleteCluster - Deletion policy to delete the RayCluster custom resource, and its Pods, on job completion.
    • DeleteWorkers - Deletion policy to delete only the worker Pods on job completion.
    • DeleteSelf - Deletion policy to delete the RayJob custom resource (and all associated resources) on job completion.
    • DeleteNone - Deletion policy to delete no resources on job completion.

Example: Run a simple Ray job with RayJob¶

Step 1: Create a Kubernetes cluster with Kind¶

Step 2: Install the KubeRay operator¶

Follow the RayCluster Quickstart to install the latest stable KubeRay operator by Helm repository.

Step 3: Install a RayJob¶

Step 4: Verify the Kubernetes cluster status¶

The KubeRay operator creates a RayCluster custom resource based on the rayClusterSpec and a submitter Kubernetes Job to submit a Ray job to the RayCluster. In this example, the entrypoint is python /home/ray/samples/sample_code.py, and sample_code.py is a Python script stored in a Kubernetes ConfigMap mounted to the head Pod of the RayCluster. Because the default value of shutdownAfterJobFinishes is false, the KubeRay operator doesn't delete the RayCluster or the submitter when the Ray job finishes.

Step 5: Check the output of the Ray job¶

The Python script sample_code.py used by entrypoint is a simple Ray script that executes a counter's increment function 5 times.

Step 6: Delete the RayJob¶

Step 7: Create a RayJob with shutdownAfterJobFinishes set to true¶

The ray-job.shutdown.yaml defines a RayJob custom resource with shutdownAfterJobFinishes: true and ttlSecondsAfterFinished: 10. Hence, the KubeRay operator deletes the RayCluster 10 seconds after the Ray job finishes. Note that the submitter job isn't deleted because it contains the ray job logs and doesn't use any cluster resources once completed. In addition, the RayJob cleans up the submitter job when the RayJob is eventually deleted due to its owner reference back to the RayJob.

Step 8: Check the RayJob status¶

Step 9: Check if the KubeRay operator deletes the RayCluster¶

Step 10: Clean up¶

Next steps¶

• RayJob Batch Inference Example
• Priority Scheduling with RayJob and Kueue
• Gang Scheduling with RayJob and Kueue